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                        数据分析
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                            <b>1.</b>
                        
                        Python数据分析内容
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                            <b>2.</b>
                        
                        python数据分析环境和工具
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                            <b>2.1.</b>
                        
                        Python数据课程 软件和环境安装
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                            <b>2.2.</b>
                        
                        python发行版
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                            <b>2.3.</b>
                        
                        交互式编辑器-JupyterNotebook
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                            <b>2.3.1.</b>
                        
                        Jupyter-notebook拓展应用
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                            <b>2.4.</b>
                        
                        包和环境管理器：conda
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                            <b>2.4.1.</b>
                        
                        pip和Virtualenv
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                            <b>2.5.</b>
                        
                        标记语言：Markdown
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                            <b>2.5.1.</b>
                        
                        Markdown语法
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                            <b>2.5.2.</b>
                        
                        文档管理工具-Gitbook
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                            <b>3.</b>
                        
                        数据分析库-Pandas
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                            <b>3.1.</b>
                        
                        pandas
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                            <b>3.2.</b>
                        
                        Series
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                            <b>3.3.</b>
                        
                        DataFrame对象-创建
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            <span><b>4.</b> 数据分析库的操作</span>
            
            
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                            <b>4.1.</b>
                        
                        DataFrame查询1-整体
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                            <b>4.2.</b>
                        
                        DataFrame查询2-专用查询
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                            <b>4.3.</b>
                        
                        DataFrame查询3-专有查询：过滤查询
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                            <b>4.4.</b>
                        
                        Pandas对象的数据操作：增删改查
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                            <b>4.4.1.</b>
                        
                        Pandas数据操作：其他操作
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                            <b>4.4.2.</b>
                        
                        Pandas数据存取
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                            <b>4.4.3.</b>
                        
                        Pandas数据运算
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                            <b>4.4.3.1.</b>
                        
                        Pandas数据运算-拓展
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                            <b>4.4.4.</b>
                        
                        Pandas分组聚合1
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                            <b>4.4.5.</b>
                        
                        Pandas分组聚合2
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                            <b>4.4.6.</b>
                        
                        Pandas数据规整-清理
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                            <b>4.4.7.</b>
                        
                        Pandas数据规整-转换
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                            <b>4.4.7.1.</b>
                        
                        离散化和面元划分
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                            <b>4.4.8.</b>
                        
                        Pandas数据规整-合并
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                            <b>4.5.</b>
                        
                        Pandas数据规整-重塑和轴向旋转
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                            <b>4.5.1.</b>
                        
                        透视表和交叉表
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                            <b>5.</b>
                        
                        Python可视化
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        <li class="chapter " data-level="5.1" data-path="Python可视化/绘图库-Matplotlib/Matplotlib常见图表.html">
            
                
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                            <b>5.1.</b>
                        
                        基础：Matplotlib常见图表
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                            <b>5.1.1.</b>
                        
                        Matplotlib常见设置和操作
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                            <b>5.2.</b>
                        
                        提升：绘图区域
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        <li class="chapter " data-level="5.3" data-path="Python可视化/绘图库-Matplotlib/2Matplotlib-图像组件.html">
            
                
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                            <b>5.3.</b>
                        
                        提升：绘图组件
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                            <b>5.4.</b>
                        
                        拓展：高级绘图
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                            <b>5.5.</b>
                        
                        拓展：数学计算展示图像
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                            <b>5.6.</b>
                        
                        拓展：注意事项
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                            <b>5.7.</b>
                        
                        拓展：pylab
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                            <b>6.</b>
                        
                        数据分析必备知识点
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        <li class="chapter " data-level="7" data-path="数据分析必备知识点/数据分析流程.html">
            
                
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                            <b>7.</b>
                        
                        数据分析流程
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<pre><code class="lang-python"><span class="hljs-keyword">import</span> numpy <span class="hljs-keyword">as</span> np
<span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd
</code></pre>
<h1 id="&#x521B;&#x5EFA;">&#x521B;&#x5EFA;</h1>
<ul>
<li><p>&#x5217;&#x8868;&#x521B;&#x5EFA;
  *ndarray &#x6570;&#x7EC4;&#x521B;&#x5EFA;</p>
</li>
<li><p>&#x5B57;&#x5178;&#x521B;&#x5EFA;</p>
<ul>
<li>&#x5B57;&#x5178;&#x5185;&#x5D4C;&#x5957;&#x5217;&#x8868;&#xFF1A;&#x8981;&#x6C42;&#x5217;&#x8868;&#x7B49;&#x957F;</li>
<li>&#x5B57;&#x5178;&#x5185;&#x5D4C;&#x5957;&#x5B57;&#x5178;&#xFF1A;&#x5185;&#x58C1;&#x5B57;&#x5178;&#x4E0D;&#x9700;&#x8981;&#x7B49;&#x957F;<ul>
<li>&#x5B57;&#x5178;&#x5185;&#x5D4C;&#x5957;Series&#xFF1A;&#x7B49;&#x540C;&#x5D4C;&#x5957;&#x5B57;&#x5178;</li>
</ul>
</li>
</ul>
</li>
</ul>
<h2 id="&#x5217;&#x8868;&#x521B;&#x5EFA;">&#x5217;&#x8868;&#x521B;&#x5EFA;</h2>
<pre><code class="lang-python">pd.DataFrame([[<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>],[<span class="hljs-number">4</span>,<span class="hljs-number">5</span>,<span class="hljs-number">6</span>],[<span class="hljs-number">7</span>,<span class="hljs-number">8</span>,<span class="hljs-number">9</span>]])
</code></pre>
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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>1</td>
      <td>2</td>
      <td>3</td>
    </tr>
    <tr>
      <th>1</th>
      <td>4</td>
      <td>5</td>
      <td>6</td>
    </tr>
    <tr>
      <th>2</th>
      <td>7</td>
      <td>8</td>
      <td>9</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">pd.DataFrame(
    [[<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>],[<span class="hljs-number">4</span>,<span class="hljs-number">5</span>,<span class="hljs-number">6</span>],[<span class="hljs-number">7</span>,<span class="hljs-number">8</span>,<span class="hljs-number">9</span>]],
    index = [<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>],
    columns = [<span class="hljs-string">&apos;a&apos;</span>,<span class="hljs-string">&apos;b&apos;</span>,<span class="hljs-string">&apos;c&apos;</span>]
)
</code></pre>
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<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
      <th>c</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>1</td>
      <td>2</td>
      <td>3</td>
    </tr>
    <tr>
      <th>2</th>
      <td>4</td>
      <td>5</td>
      <td>6</td>
    </tr>
    <tr>
      <th>3</th>
      <td>7</td>
      <td>8</td>
      <td>9</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">a = pd.DataFrame([
    [<span class="hljs-string">&apos;&#x5218;&#x53CC;&#x4E39;&apos;</span>,<span class="hljs-number">18</span>,<span class="hljs-number">177.6</span>,<span class="hljs-number">66</span>,<span class="hljs-keyword">True</span>],
    [<span class="hljs-string">&apos;&#x4E01;&#x6D01;&apos;</span>,<span class="hljs-number">28</span>,<span class="hljs-number">166.4</span>,<span class="hljs-number">45</span>,<span class="hljs-keyword">False</span>],
],
    index = [<span class="hljs-number">0</span>,<span class="hljs-number">1</span>],
    columns = [<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;age&apos;</span>,<span class="hljs-string">&apos;height&apos;</span>,<span class="hljs-string">&apos;weight&apos;</span>,<span class="hljs-string">&apos;gender&apos;</span>],
)
a
</code></pre>
<div>
<style scoped>
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        vertical-align: middle;
    }

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    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>age</th>
      <th>height</th>
      <th>weight</th>
      <th>gender</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&#x5218;&#x53CC;&#x4E39;</td>
      <td>18</td>
      <td>177.6</td>
      <td>66</td>
      <td>True</td>
    </tr>
    <tr>
      <th>1</th>
      <td>&#x4E01;&#x6D01;</td>
      <td>28</td>
      <td>166.4</td>
      <td>45</td>
      <td>False</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python"><span class="hljs-comment">#ndarray &#x6570;&#x7EC4;&#x521B;&#x5EFA;&#xFF0C;&#x7C7B;&#x4F3C;&#x5217;&#x8868;</span>
np.arange(<span class="hljs-number">10</span>)
</code></pre>
<pre><code>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
</code></pre><pre><code class="lang-python">x = np.arange(<span class="hljs-number">10</span>).reshape(<span class="hljs-number">2</span>,<span class="hljs-number">5</span>)
x
</code></pre>
<pre><code>array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
</code></pre><pre><code class="lang-python">pd.DataFrame(x)
</code></pre>
<div>
<style scoped>
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    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>0</th>
      <th>1</th>
      <th>2</th>
      <th>3</th>
      <th>4</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>0</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>4</td>
    </tr>
    <tr>
      <th>1</th>
      <td>5</td>
      <td>6</td>
      <td>7</td>
      <td>8</td>
      <td>9</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python">pd.DataFrame(x,index = [<span class="hljs-number">5</span>,<span class="hljs-number">10</span>],columns=[<span class="hljs-string">&apos;a&apos;</span>,<span class="hljs-string">&apos;b&apos;</span>,<span class="hljs-string">&apos;c&apos;</span>,<span class="hljs-string">&apos;d&apos;</span>,<span class="hljs-string">&apos;e&apos;</span>])
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

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        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>a</th>
      <th>b</th>
      <th>c</th>
      <th>d</th>
      <th>e</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>5</th>
      <td>0</td>
      <td>1</td>
      <td>2</td>
      <td>3</td>
      <td>4</td>
    </tr>
    <tr>
      <th>10</th>
      <td>5</td>
      <td>6</td>
      <td>7</td>
      <td>8</td>
      <td>9</td>
    </tr>
  </tbody>
</table>
</div>



<h2 id="&#x5B57;&#x5178;&#x521B;&#x5EFA;">&#x5B57;&#x5178;&#x521B;&#x5EFA;</h2>
<h4 id="&#x5B57;&#x5178;&#x5185;&#x5D4C;&#x5957;&#x5217;&#x8868;">&#x5B57;&#x5178;&#x5185;&#x5D4C;&#x5957;&#x5217;&#x8868;</h4>
<pre><code class="lang-python">b = pd.DataFrame({
    <span class="hljs-string">&apos;name&apos;</span>: [<span class="hljs-string">&apos;&#x5218;&#x53CC;&#x4E39;&apos;</span>,<span class="hljs-string">&apos;&#x674E;&#x5C27;&apos;</span>,<span class="hljs-string">&apos;&#x8881;&#x9E4F;&#x98DE;&apos;</span>],
    <span class="hljs-string">&apos;age&apos;</span>: [<span class="hljs-number">18</span>,<span class="hljs-number">26</span>,<span class="hljs-number">28</span>],
    <span class="hljs-string">&apos;height&apos;</span>: [<span class="hljs-number">177.4</span>,<span class="hljs-number">165.4</span>,<span class="hljs-number">180.1</span>],
    <span class="hljs-string">&apos;weight&apos;</span>: [<span class="hljs-number">45</span>,<span class="hljs-number">66</span>,<span class="hljs-number">54</span>],
    <span class="hljs-string">&apos;gender&apos;</span>: [<span class="hljs-keyword">True</span>,<span class="hljs-keyword">False</span>,<span class="hljs-keyword">True</span>],
})
b
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>age</th>
      <th>height</th>
      <th>weight</th>
      <th>gender</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>0</th>
      <td>&#x5218;&#x53CC;&#x4E39;</td>
      <td>18</td>
      <td>177.4</td>
      <td>45</td>
      <td>True</td>
    </tr>
    <tr>
      <th>1</th>
      <td>&#x674E;&#x5C27;</td>
      <td>26</td>
      <td>165.4</td>
      <td>66</td>
      <td>False</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x8881;&#x9E4F;&#x98DE;</td>
      <td>28</td>
      <td>180.1</td>
      <td>54</td>
      <td>True</td>
    </tr>
  </tbody>
</table>
</div>



<h3 id="&#x81EA;&#x5B9A;&#x4E49;&#x884C;&#x7D22;&#x5F15;&#xFF0C;&#x548C;&#x5217;&#x7D22;&#x5F15;">&#x81EA;&#x5B9A;&#x4E49;&#x884C;&#x7D22;&#x5F15;&#xFF0C;&#x548C;&#x5217;&#x7D22;&#x5F15;</h3>
<pre><code class="lang-python">b = pd.DataFrame({
    <span class="hljs-string">&apos;name&apos;</span>: [<span class="hljs-string">&apos;&#x5218;&#x53CC;&#x4E39;&apos;</span>,<span class="hljs-string">&apos;&#x674E;&#x5C27;&apos;</span>,<span class="hljs-string">&apos;&#x8881;&#x9E4F;&#x98DE;&apos;</span>],
    <span class="hljs-string">&apos;age&apos;</span>: [<span class="hljs-number">18</span>,<span class="hljs-number">26</span>,<span class="hljs-number">28</span>],
    <span class="hljs-string">&apos;height&apos;</span>: [<span class="hljs-number">177.4</span>,<span class="hljs-number">165.4</span>,<span class="hljs-number">180.1</span>],
    <span class="hljs-string">&apos;weight&apos;</span>: [<span class="hljs-number">45</span>,<span class="hljs-number">66</span>,<span class="hljs-number">54</span>],
    <span class="hljs-string">&apos;gender&apos;</span>: [<span class="hljs-keyword">True</span>,<span class="hljs-keyword">False</span>,<span class="hljs-keyword">True</span>],
},
    index = [<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>],
    columns = [<span class="hljs-string">&apos;name&apos;</span>,<span class="hljs-string">&apos;age&apos;</span>,<span class="hljs-string">&apos;height&apos;</span>,<span class="hljs-string">&apos;sex&apos;</span>]
)
b
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>age</th>
      <th>height</th>
      <th>sex</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5218;&#x53CC;&#x4E39;</td>
      <td>18</td>
      <td>177.4</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x674E;&#x5C27;</td>
      <td>26</td>
      <td>165.4</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x8881;&#x9E4F;&#x98DE;</td>
      <td>28</td>
      <td>180.1</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
</div>



<h2 id="&#x5B57;&#x5178;&#x5D4C;&#x5957;&#x5B57;&#x5178;">&#x5B57;&#x5178;&#x5D4C;&#x5957;&#x5B57;&#x5178;</h2>
<p>&#x5B57;&#x5178;&#x4E0D;&#x8981;&#x6C42;&#x7B49;&#x957F;</p>
<pre><code class="lang-python">c = pd.DataFrame({
    <span class="hljs-string">&apos;name&apos;</span>: {<span class="hljs-number">2</span>: <span class="hljs-string">&apos;&#x5218;&#x53CC;&#x4E39;&apos;</span>,<span class="hljs-number">5</span>: <span class="hljs-string">&apos;&#x674E;&#x5C27;&apos;</span>,<span class="hljs-number">8</span>: <span class="hljs-string">&apos;&#x8881;&#x9E4F;&#x98DE;&apos;</span>},
    <span class="hljs-string">&apos;age&apos;</span>: {<span class="hljs-number">2</span>: <span class="hljs-number">18</span>,<span class="hljs-number">5</span>: <span class="hljs-number">26</span>,<span class="hljs-number">8</span>: <span class="hljs-number">28</span>},
    <span class="hljs-string">&apos;height&apos;</span>: {<span class="hljs-number">2</span>: <span class="hljs-number">177.4</span>,<span class="hljs-number">5</span>: <span class="hljs-number">165.4</span>,<span class="hljs-number">8</span>: <span class="hljs-number">180.1</span>},
    <span class="hljs-string">&apos;weight&apos;</span>: {<span class="hljs-number">2</span>: <span class="hljs-number">45</span>,<span class="hljs-number">5</span>: <span class="hljs-number">66</span>}, <span class="hljs-comment"># &#x5C11;&#x4E00;&#x4E2A;&#x503C;</span>
    <span class="hljs-string">&apos;gender&apos;</span>: {<span class="hljs-number">2</span>: <span class="hljs-keyword">True</span>,<span class="hljs-number">5</span>: <span class="hljs-keyword">False</span>,<span class="hljs-number">8</span>: <span class="hljs-keyword">True</span>},
})
c
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>age</th>
      <th>height</th>
      <th>weight</th>
      <th>gender</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>2</th>
      <td>&#x5218;&#x53CC;&#x4E39;</td>
      <td>18</td>
      <td>177.4</td>
      <td>45.0</td>
      <td>True</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x674E;&#x5C27;</td>
      <td>26</td>
      <td>165.4</td>
      <td>66.0</td>
      <td>False</td>
    </tr>
    <tr>
      <th>8</th>
      <td>&#x8881;&#x9E4F;&#x98DE;</td>
      <td>28</td>
      <td>180.1</td>
      <td>NaN</td>
      <td>True</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python"><span class="hljs-comment"># Series&#x7EC4;&#x6210;&#x7684;&#x5B57;&#x5178; &#x521B;&#x5EFA;DataFrame&#xFF0C;&#x540C;&#x5D4C;&#x5957;&#x5B57;&#x5178;</span>

<span class="hljs-comment"># &#x5916;&#x5C42;&#x952E;&#x662F;&#x5217;&#x7D22;&#x5F15;&#xFF0C;&#x5185;&#x5C42;&#x952E;&#x662F;&#x884C;&#x7D22;&#x5F15;</span>
h_values = {
    <span class="hljs-string">&apos;name&apos;</span>:pd.Series([<span class="hljs-string">&apos;&#x5C0F;&#x660E;&apos;</span>, <span class="hljs-string">&apos;&#x5C0F;&#x534E;&apos;</span>, <span class="hljs-string">&apos;&#x5C0F;&#x7EA2;&apos;</span>, <span class="hljs-string">&apos;&#x5C0F;&#x9751;&apos;</span>, <span class="hljs-string">&apos;&#x5C0F;&#x5170;&apos;</span>],index=[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">5</span>]),
    <span class="hljs-string">&apos;sex&apos;</span>:pd.Series([<span class="hljs-number">1</span>, <span class="hljs-number">0</span>, <span class="hljs-number">0</span>, <span class="hljs-number">1</span>, <span class="hljs-number">0</span>],index=[<span class="hljs-number">1</span>, <span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">5</span>]),
    <span class="hljs-string">&apos;age&apos;</span>:pd.Series([<span class="hljs-number">28</span>, <span class="hljs-number">38</span>, <span class="hljs-number">48</span>, <span class="hljs-number">8</span>],index=[<span class="hljs-number">2</span>, <span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">5</span>])  <span class="hljs-comment"># &#x5C11;&#x4E00;&#x4E2A;&#x503C;&#x81EA;&#x52A8;&#x586B;&#x5145;&#x4E3A;NaN</span>
}
h = pd.DataFrame(h_values)

h
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>1</th>
      <td>&#x5C0F;&#x660E;</td>
      <td>1</td>
      <td>NaN</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>0</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>0</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>1</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>5</th>
      <td>&#x5C0F;&#x5170;</td>
      <td>0</td>
      <td>8.0</td>
    </tr>
  </tbody>
</table>
</div>




<pre><code class="lang-python"><span class="hljs-comment"># &#x6307;&#x5B9A;&#x5185;&#x5C42;&#x5B57;&#x5178;&#x952E;&#xFF08;&#x884C;&#x7D22;&#x5F15;&#xFF09;&#xFF0C;&#x6CA1;&#x6709;&#x7684;&#x503C;&#x4F1A;&#x586B;&#x5145;NaN</span>
i = pd.DataFrame(h_values, index=[<span class="hljs-number">3</span>, <span class="hljs-number">4</span>, <span class="hljs-number">2</span>, <span class="hljs-number">6</span>])
i
</code></pre>
<div>
<style scoped>
    .dataframe tbody tr th:only-of-type {
        vertical-align: middle;
    }

    .dataframe tbody tr th {
        vertical-align: top;
    }

    .dataframe thead th {
        text-align: right;
    }
</style>
<table border="1" class="dataframe">
  <thead>
    <tr style="text-align: right;">
      <th></th>
      <th>name</th>
      <th>sex</th>
      <th>age</th>
    </tr>
  </thead>
  <tbody>
    <tr>
      <th>3</th>
      <td>&#x5C0F;&#x7EA2;</td>
      <td>0.0</td>
      <td>38.0</td>
    </tr>
    <tr>
      <th>4</th>
      <td>&#x5C0F;&#x9751;</td>
      <td>1.0</td>
      <td>48.0</td>
    </tr>
    <tr>
      <th>2</th>
      <td>&#x5C0F;&#x534E;</td>
      <td>0.0</td>
      <td>28.0</td>
    </tr>
    <tr>
      <th>6</th>
      <td>NaN</td>
      <td>NaN</td>
      <td>NaN</td>
    </tr>
  </tbody>
</table>
</div>



                    
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